As affected by shadowing and multi-path propagation, wireless channel states (e.g., qualities) are time-varying and time-correlated [1]}. Since wireless channel dynamics have significant impact on the remote estimation stability, accurate channel modeling is critical for stability analysis.
In the multi-system scheduling works above, time-invariant channels were considered in [2]}, [3]}; time-uncorrelated fading channels were adopted in [4]}, [5]}; recently, more practical time-correlated fading channels were applied in [6]}, [7]} and modeled by Markov processes.
However, the Markov modeling is suitable for fast fading channels, i.e., the channel state changes at each time, and is not accurate for the slow fading scenario.
As verified by experiments in industrial environment [8]}, semi-Markov process, which is a generalized model of the conventional Markov process, is suitable for characterizing slow fading channels in factories.
A semi-Markov process can capture both the random holding period of each channel state and the state transitions.
